1.软件版本

matlab2013b

2.本算法理论知识

3.核心代码

clc
clear
close all;
warning off;
addpath 'functions'
 

%外部输入的标准的值，这里可以在具体的验证的时候加入扰动。
CR   = 0.4;
h    = 0.6;
selt = 1;%1:加入扰动；0：不加扰动



%以下两个值越大，那么其PSO优化的RBF性能就越好
%进化次数  
iteration  = 250;
%种群规模
Sizes      = 20;   

sel        = 0;%1进行PSO优化得到最佳值,0直接进行实际测试

%本代码是在普通的PSO下的RBF神经网络解耦程序
%本代码是在普通的PSO下的RBF神经网络解耦程序
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%以下为PSO%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if sel == 1
   %输入的CR和h
   %分别仅输入CR和h，使其达到解耦的结果
   [yy,Zbest] = func_train_online(iteration,Sizes,CR,h);
   figure(1)
   plot(yy,'LineWidth',2);grid on;
   xlabel('进化代数');
   ylabel('适应度');
   individual=Zbest;
   save trainPSO.mat Zbest yy
else
   load trainPSO.mat
   figure(1)
   plot(yy,'LineWidth',2);grid on;
   xlabel('进化代数');
   ylabel('适应度');
   individual=Zbest; 
end


 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%以下为RBF%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
w11=reshape(individual(1:6),3,2);
w12=reshape(individual(7:12),3,2);
w13=reshape(individual(13:18),3,2);

w21=individual(19:27);
w22=individual(28:36);
w23=individual(37:45);

rate1=0.006;rate2=0.001; %学习率
k=0.3;K=3;
y_1=zeros(3,1);y_2=y_1;y_3=y_2;   %输出值
u_1=zeros(3,1);u_2=u_1;u_3=u_2;   %控制率
h1i=zeros(3,1);h1i_1=h1i;  %第一个控制量
h2i=zeros(3,1);h2i_1=h2i;  %第二个控制量
h3i=zeros(3,1);h3i_1=h3i;  %第三个空置量
x1i=zeros(3,1);x2i=x1i;x3i=x2i;x1i_1=x1i;x2i_1=x2i;x3i_1=x3i;   %隐含层输出 

%权值初始化
k0=0.03;

%值限定
ynmax=1;ynmin=-1;  %系统输出值限定
xpmax=1;xpmin=-1;  %P节点输出限定
qimax=1;qimin=-1;  %I节点输出限定
qdmax=1;qdmin=-1;  %D节点输出限定
uhmax=1;uhmin=-1;  %输出结果限定

for k=1:1:6000
    k
    %系统输出
    y1(k) = (0.4*y_1(1)+u_1(1)/(1+u_1(1)^2)+0.2*u_1(1)^3+0.5*u_1(2))+0.3*y_1(2);
    y2(k) = (0.4*y_1(2)+u_1(2)/(1+u_1(2)^2)+0.2*u_1(2)^3+0.5*u_1(1))+0.3*y_1(1);
    y3(k) = 0;
    
    
    %控制目标
    if selt == 1%加扰测试
        r1(k)  = CR + 0.005*sin(2*pi*k/200);
        r2(k)  = h  + 0.015*sin(2*pi*k/200);
        r3(k)  = 0; 
        r1s(k) = CR;
        r2s(k) = h;
        r3s(k) = 0;         
    else        %跟踪测试
        r1(k)  = sign(0.001*sin(2*pi*k/2000));
        r2(k)  = sign(0.003*sin(2*pi*k/2000));
        r3(k)  = 0;      
        r1s(k) = CR;
        r2s(k) = h;
        r3s(k) = 0;    
    end
    
    
    %系统输出限制
    yn=[y1(k),y2(k),y3(k)];
    yn(find(yn>ynmax))=ynmax;
    yn(find(ynxpmax))=xpmax;
    xp(find(xpqimax))=qimax;
    qi(find(qiqdmax))=qdmax;
    qd(find(qduhmax))=uhmax;
    uh(find(uh

4.操作步骤与仿真结论

普通PSO下的RBF解耦仿真结果：